One frequently collected sequential flow of data may be electronic transactions of consumers. When an individual engages in electronic transactions (e.g., via use of credit card technologies, mobile pay, online payment accounts, etc.) throughout a given period of time, the transaction data for the particular individual is collected, monitored, and stored sequentially, at a server of the issuer of the transaction instrument. Thus, such a server may leverage the vast amount of sequentially stored transaction data in electronic repositories, to provide intelligent, electronically generated feedback in an automated environment. Especially with artificial neural networks (e.g., RNN) having capabilities to be trained on sequential inputs, it is highly desirable for a server to practically apply machine learning, to recognize patterns in consumer spending behaviors, and provide feedback accordingly.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section.